Methods and Apparatuses for Predicting User Destinations

ABSTRACT

The present disclosure relates to a concept for machine-learning-based prediction of a destination for a user. Based on historic search data associated with the user, at least one candidate destination is determined based on the user and a given context. A plurality of embedding vectors are determined from an embedding matrix, wherein the embedding vectors are associated with the at least one candidate destination, the user, and the given context. The embedding matrix comprising embedding vectors for different components of the historic search data. The plurality of embedding vectors are fed into one or more first neural network layers to generate a semantic embedding for the candidate destination. The semantic embedding is into one or more second neural network layers to generate a probability score for the candidate destination.

FIELD

The present disclosure generally relates to location finding andnavigation and, more particularly, methods and apparatuses forpredicting destinations for a user based on the user (user profile), agiven context, and historic location data associated with the user.

BACKGROUND

Conventional navigational search algorithms are based on user's searchrecency, proximity and popularity. The search destination can be arecent searched location (recall), a given destination the user did notknow before (find), or a recommended place the user might be interestedin (discover).

Search engines of industrial prediction systems usually require input bykeypad or by voice to start search among locations. After typing aprefix of the target location's name such as ‘c’ for a location staringwith “c” or uttering the target location's name, the search engine willreturn a list of candidate locations ranked by the algorithm. If thetarget is not within the list, the user may either keep typing theremaining letters or rephrase the utterance. When the search process isin an initial stage without input by the user to the search engine,conventional services may provide a list of locations based on the usersearch history and favorite places such as home and office.

To optimize a ranking of the returned list of search results, a varietyof models or algorithm are required to extract different aspects of usersearch behaviors. For example, temporal information can be representedby the recency of a searched location. Once a target location isreturned and selected by the user, the location information can be sentto a downstream application, e.g., smart navigation and notification forroute information.

As a user's location search behavior (historic location data) may berecorded as a sequential event of locations ordered by timestamps,contextual features can be revealed to model the behavior pattern thenyielding a better ranking of the potential location candidates.Contextual modeling is popular in both academic and industrial area toenable human behavior learning of the sequential event input such asonline purchase data, trip data, and check-in data. Contextual featuresmay contain spatial information, e.g., current location, when a userdoing the search and temporal information e.g., date and time of thesearch event, recency information of previous search events.

It is desired to provide a context-sensitive search concept to furtherimprove a user's search experience.

SUMMARY

According to a first aspect of the present disclosure, it is provided acomputer-implemented method for predicting a destination for a user. Themethod includes determining, based on information on the user and agiven search context, at least one candidate destination from historiclocation data associated with the user. The method further includesdetermining, from an embedding matrix, a plurality of embedding vectorsassociated with the at least one candidate destination, the user, andthe given context. Here, “determining” may be understood as training theplurality of embedding vectors and/or selecting pre-trained embeddingvectors, depending on the state of the machine learning model. Theembedding matrix comprises embedding vectors for different components ofthe historic location data (e.g., different historic users, differenthistoric locations, different historic contexts, etc.). The methodfurther includes feeding the plurality of embedding vectors into one ormore context modeling machine learning network layers to generate asemantic embedding for the candidate destination. Here, the plurality ofembedding vectors may be mapped to a single semantic embedding vectorfor the candidate destination. The method further includes feeding thesemantic embedding into one or more ranking machine learning layers togenerate a probability score for the candidate destination.

Thus, the user (e.g. a user profile, a user index, etc.), the givensearch context, and the user's search history may act as raw input datafor the destination prediction. The user and the given search contextmay lead to a selection of one or more candidate destinations (potentialdestinations) from the historic location or search data. Each of thecandidate destinations, together with the user and the given context,may then be translated to a respective plurality of embedding vectors.Here, each of the embedding vectors may be associated with (e.g.,describe) a certain aspect of the candidate destination, the user, andthe given context. That is, different components of the raw input (user,context, location) may be encoded to respective embedding vectors. Theskilled person having benefit from the present disclosure willappreciate that an embedding is a relatively low-dimensional space intowhich one can translate high-dimensional vectors. An embedding is amapping from discrete objects, such as users, locations, context (e.g.time, current location), words (e.g., search strings), to vectors ofreal numbers. The individual dimensions in these vectors typically haveno inherent meaning. Instead, it is the overall patterns of location anddistance between vectors that machine learning takes advantage of.Embeddings make it easier to do machine learning on large inputs likesparse vectors representing words (search queries) or locations.Ideally, an embedding captures some of the semantics of the input byplacing semantically similar inputs close together in the embeddingspace. An embedding can be learned and reused across models. Theplurality of embedding vectors is then fed to a machine learning contextmodeling network to construct a semantic embedding for the candidatedestination by mapping the plurality of embedding vectors representingdifferent raw inputs to a single semantic embedding vector representingthe candidate destination. The respective semantic embeddings for aplurality of candidate destinations may then be fed into a machinelearning ranking network to predict the target destination bydetermining the highest probability score among all candidatedestinations.

In some embodiments, the historic search or location data associatedwith the user may be based on past data, among all possible destinationsfrom difference sources, such as, for example, previously visitedlocations, a search history and a search from a digital map. Historiclocation data may be available for a plurality of different users.Historic location data may be stored in one or more local or remoteelectronic data storages to which the machine learning circuit hasaccess.

In some embodiments, the context may indicative of at least one of acurrent time, a current location of the user, current weatherconditions, current traffic conditions. The skilled person havingbenefit from the present disclosure will appreciate that the context ofthe user's search can include additional or alternative features anddepend on the specific implementation. In some embodiments, the contextcomponent “current time” may be further decomposed into two features“hour of day” and “day of week”. Similarly, the context component“current weather conditions” may be decomposed into “weather atorigin”/“weather at destination”. Similarly, the context component“current traffic conditions” may be decomposed into “traffic atorigin”/“traffic at destination”, for example.

In some embodiments, the at least one candidate destination may bedetermined from at least one of the user's personal search history, aquery of the user (e.g., by typing or by voice recognition), or thegiven context (e.g., time, current user location). In other words, theat least one candidate destination may be determined from only theuser's personal search history, only a query of the user, only the givencontext, or a combination thereof

In some embodiments, the method may be performed for a plurality ofcandidate destinations to generate a respective probability score foreach of the candidate destinations. This may be done in parallel oriteratively for the plurality of candidate destination.

In some embodiments, the method further includes predicting the user'starget destination by choosing the candidate destination having thehighest computed probability score or the highest rank among theplurality of candidate destinations. The candidate destination havingthe highest computed probability score may be listed first. Furthercandidate destinations may be listed in accordance with their respectiveprobability scores. The candidate destination having the second highestcomputed probability score may be listed as second item, etc.

In some embodiments, the method further includes adjusting (training)computational weights of the context modeling and/or the ranking machinelearning layers if the user (manually) selects a candidate destinationas target destination not having the highest probability score or thehighest rank to minimize a difference between the machine learning modelprediction and ground truth corresponding to the user's selection. Here,the user's selection may act as ground truth for modifying the machinelearning model so as to output a ranking list with the user's selectionas top candidate destination. In this way, the machine learning modelmay be trained online (during operation).

In some embodiments, the method further includes training the embeddingvectors of the embedding matrix based on the historic location data byadjusting computational weights of one or more feature processing layersof the machine learning model to map semantically similar components ofthe historic location data to geometrically close embedding vectors in acommon semantic space. For example, this may be done together withtraining the context modeling and/or the ranking layers of the machinelearning model.

The feature processing (embedding), context, and ranking machinelearning model(s) can be trained together offline using the historiclocation data for training, validation and test. During onlineapplication, the user's search data may be continuously collected formodel adaptation. In this phase, there may be several options, e.g.jointly train together. We can also adapt contextual ranking only ondaily basis, and joint training on weekly or monthly basis, etc. In thiscase, training for embedding and contextual ranking can be flexiblymanaged, sometimes adapt ranking only and faster, and jointly adapt thema bit slower, etc.

In some embodiments, the embedding matrix comprises U user embeddingvectors corresponding to U users in the location history, Q locationembedding vectors corresponding to Q searched locations in the locationhistory, K category embedding vectors corresponding to K destinationcategories in the location history, 24 hour embedding vectorscorresponding to 24 hours per day, and seven day embedding vectorscorresponding to seven days per week. The skilled person having benefitfrom the present disclosure will appreciate that other implementationsare possible.

According to a further aspect of the present disclosure, it is providedan apparatus for predicting a destination for a user. The apparatuscomprises circuitry configured to determine, based on the user and agiven context, at least one candidate destination from historic locationdata associated with the user. The circuitry is further configured todetermine, from an embedding matrix, a plurality of embedding vectorsassociated with the at least one candidate destination, the user, andthe given context. The embedding matrix comprises embedding vectors fordifferent components of the historic location data. The circuitrycomprises one or more first (context modeling) machine learning networklayers configured to generate a semantic embedding for the candidatedestination based on the determined plurality of embedding vectors. Thecircuitry further comprises one or more second (ranking) machinelearning network layers configured to generate a probability score forthe candidate destination based on the semantic embedding for thecandidate destination.

In some embodiments, the one or more first (context modeling) machinelearning network layers may be implemented as a convolutional neuralnetwork (CNN). The one or more second (ranking) machine learning layersmay be implemented as a CNN or a fully connected neural network, forexample.

In some embodiments, the (machine learning) circuitry is configured togenerate a respective probability score for a plurality of candidatedestinations and to predict the user's target destination by choosingthe candidate destination having the highest probability score.

In some embodiments, the (machine learning) circuitry is configured toadjust (train) computational weights of the first and/or the secondmachine learning network layers if the user selects a candidatedestination as target destination not having the highest probabilityscore.

This sort of training could be performed during user operation of theapparatus, for example.

In some embodiments, the (machine learning) circuitry is configured totrain the embedding matrix based on the historic location data (searchhistory) by adjusting computational weights of one or more third machinelearning network layers to map semantically similar components of thesearch history to geometrically close embedding vectors. The trainingdata would be different users and respective contexts together withrelated target destinations. The feature processing layer, the contextmodeling layer, and the ranking layer may be trained together in orderto minimize a difference between the model prediction and ground truthcorresponding to the user's target destination from the search history.

According to a further aspect of the present disclosure, it is provideda vehicle. The vehicle comprises (machine learning) circuitry configuredto determine, based on the user and a given context, at least onecandidate destination from historic location data associated with theuser. The circuitry is further configured to determine, from anembedding matrix, a plurality of embedding vectors associated with theat least one candidate destination, the user, and the given context. Theembedding matrix comprises embedding vectors for different (categorial)components of the historic location data. The circuitry comprises one ormore first (context modeling) machine learning network layers configuredto generate a semantic embedding for the candidate destination based onthe determined plurality of embedding vectors. The circuitry furthercomprises one or more second (ranking) machine learning layersconfigured to generate a probability score for the candidate destinationbased on the semantic embedding for the candidate destination. Thecircuitry is configured to generate a respective probability score for aplurality of candidate destinations and to predict the user's targetdestination by choosing the candidate destination having the highestprobability score.

The present disclosure proposes a contextual, personalized andcollaborative algorithmic framework for the smart search based on usersearch behavior learning.

BRIEF DESCRIPTION OF THE FIGURES

Some examples of apparatuses and/or methods will be described in thefollowing by way of example only, and with reference to the accompanyingfigures, in which

FIG. 1 shows a block diagram of an apparatus for predicting adestination for the user in accordance with the present disclosure;

FIG. 2 shows a high-level overview of the proposed algorithm framework;

FIG. 3 illustrates an example of a context modeling layer;

FIG. 4 illustrates an example of a ranking layer;

FIG. 5 shows an example of returned search results;

FIG. 6 illustrates a car comprising an apparatus for predicting adestination for the user in accordance with the present disclosure; and

FIG. 7 shows a flowchart of a method for predicting a destination forthe user in accordance with the present disclosure.

DETAILED DESCRIPTION

Some examples are now described in more detail with reference to theenclosed figures. However, other possible examples are not limited tothe features of these embodiments described in detail. Other examplesmay include modifications of the features as well as equivalents andalternatives to the features. Furthermore, the terminology used hereinto describe certain examples should not be restrictive of furtherpossible examples.

Throughout the description of the figures same or similar referencenumerals refer to same or similar elements and/or features, which may beidentical or implemented in a modified form while providing the same ora similar function. The thickness of lines, layers and/or areas in thefigures may also be exaggerated for clarification.

When two elements A and B are combined using an ‘or’, this is to beunderstood as disclosing all possible combinations, i.e. only A, only Bas well as A and B, unless expressly defined otherwise in the individualcase. As an alternative wording for the same combinations, “at least oneof A and B” or “A and/or B” may be used. This applies equivalently tocombinations of more than two elements.

If a singular form, such as “a”, “an” and “the” is used and the use ofonly a single element is not defined as mandatory either explicitly orimplicitly, further examples may also use several elements to implementthe same function. If a function is described below as implemented usingmultiple elements, further examples may implement the same functionusing a single element or a single processing entity. It is furtherunderstood that the terms “include”, “including”, “comprise” and/or“comprising”, when used, describe the presence of the specifiedfeatures, integers, steps, operations, processes, elements, componentsand/or a group thereof, but do not exclude the presence or addition ofone or more other features, integers, steps, operations, processes,elements, components and/or a group thereof.

An objective of the present disclosure may be, for a given user (u),context (c), and optional query (q), find the best ranked destinations(d) the user may look for. For example, a user (u) types, e.g. “c” (=q),for a given location (l) and time (t), yielding input data (u, l, t, q).

FIG. 1 shows a block diagram of an apparatus 100 for predicting adestination for the user in accordance with the present disclosure,given the input data (u, l, t, q).

Apparatus 100 comprises machine learning circuitry 110 implementing oneor more machine-learning algorithms configured to model a user's searchpreference based on information on the user (u), a given search context(l, t, q), and based on historic location (search) data associated withthe user and/or other users.

At an input stage, machine learning circuitry 110 is configured todetermine, based on the information 105 on the user and the given searchcontext, at least one candidate destination from historic location data107 associated with the user. At the input stage, the information 105 onthe user, the given search context, and the candidate destination may beused as pointers to entries of an embedding matrix comprising aplurality of embedding vectors. Each of the inputs 105, 107 may thus bemapped to a corresponding embedding vector of real numbers. Thus,machine learning circuitry 110 is further configured to determine, fromthe embedding matrix, a plurality of embedding vectors associated withthe at least one candidate destination, the user, and the given searchcontext. The embedding matrix comprises embedding vectors for differentcomponents or aspects of the input data 105 and the candidatedestination determined from historic location data 107. The skilledperson having benefit from the present disclosure will appreciate thatthe embedding vectors of the embedding matrix may have to be trainedduring a training phase. The mapping from raw input 105 to correspondingembedding vectors will also be referred to as feature processing layerof machine learning circuitry 110 in the sequel.

The plurality of embedding vectors representing the raw input 105 andthe at least one candidate destination from historic location data 107can be regarded as output of the feature processing layer and may serveas input for a subsequent context modeling layer of machine learningcircuitry 110. The context modeling layer of machine learning circuitry110 is configured to generate a semantic embedding for the candidatedestination based on the plurality of embedding vectors determined bythe feature processing layer. For example, the context modeling layer ofmachine learning circuitry 110 may be implemented as a convolutionalneural network (CNN). However, other network topologies may apply aswell, depending on the underlying problem to solve. Output of thecontext modeling layer of machine learning circuitry 110 is a semanticembedding vector for each candidate destination.

The semantic embedding vectors for the candidate destinations may thenserve as input for a ranking layer of machine learning circuitry 110which is configured to generate a probability score for each candidatedestination based on the semantic embedding (vector) for the respectivecandidate destinations. For example, the ranking layer of machinelearning circuitry 110 may be implemented as a CNN or as a fullyconnected neural network. However, the skilled person having benefitfrom the present disclosure will appreciate that other networktopologies may apply as well, depending on the underlying specificproblem to solve.

Output of the ranking layer of machine learning circuitry 110 may be aranking 115 of a plurality of candidate destinations the based on theinput information 105 on the user and the given search context as wellas the historic location data 107. The candidate destination having thehighest probability according to machine learning circuitry 110modelling the user's search preference may be ranked first. Thecandidate destination having the second highest probability may beranked second, and so on.

In case the predicted candidate location having the highest probabilityactually matches the user's true search preference or true searchintention (ground truth) d_(s), machine learning circuitry 110 modelsthe user's search preference well. However, if the user selects anotherdestination as true search intention (ground truth) d_(s) than thepredicted candidate location having the highest probability score, themachine learning model needs to be adapted until a loss cost function120 between ranking 115 and ground truth d_(s) is minimum. Thus,training of machine learning circuitry 110 can also happen duringoperation of apparatus 100. The skilled person having benefit from thepresent disclosure will appreciate that machine learning circuitry 110may initially be trained based on search histories (training data) fordifferent users, respective search contexts, and respective intendeddestinations.

FIG. 2 provides a high-level overview of the proposed algorithmframework.

The input information 105 on the user and the given search context aswell as the candidate destination from historic location data 107 istranslated into corresponding embedding vectors 202-1, . . . , 202-N(here: N=5). For example, the user may be mapped to embedding vector202-1. In the illustrated example search context “search time” issubdivided into “hour of day (HOD)” and “day of week (DOW)”. HOD may bemapped to embedding vector 202-2, DOW may be mapped to embedding vector202-3. Search context “current location of user (L)” may be mapped toembedding vector 202-4. A category of the candidate destination (CATE)may be mapped to embedding vector 202-5. The skilled person havingbenefit from the present disclosure will appreciate that the mapping ofraw input to feature/embedding vectors illustrated in FIG. 2 is of mereillustrative nature and that other contexts and/or mappings areconceivable.

The embedding vectors 202-1, . . . , 202-N output from featureprocessing layer of machine learning circuitry 110 are input to contextmodeling layer which translates the embedding vectors 202-1, . . . ,202-N representing the inputs to a semantic embedding vector 204 for therespective candidate destination. For this purpose, the plurality ofembedding vectors 202-1, . . . , 202-N may be weighted with respectivecomputational weights 206 of context modeling layer which have to betrained.

Semantic embedding vectors 204-1, . . . , 204-n for respective ncandidate destinations are fed into a ranking neural network 210 makingup to deliver a ranking 215 of the plurality of candidate destinationsl₀, l₁, . . . , l_(n). The proposed system is trainable based on theloss defined by ranking task, e.g. rank locations.

In the following, a more detailed description of the various layers ofmachine learning circuitry 110 will be provided.

Feature Processing Layer

For example, a user search event may be defined as searching a certainlocation at a given temporal context. Here, temporal context C may befurther decomposed into two features: hour of day HOD and day of weekDOW. All users U, potential locations (destinations) L, destinationcategory Cate, and temporal context C may be modeled to construct thefeature processing layer comprising the raw input. To encode spatial andtemporal context information, representation learning may be applied togenerate respective embedding vectors for different objects orcomponents in the search history 107. An embedding is a mapping of adiscrete categorical variable to a vector of continuous numbers.Therefore, a semantic meaning can be enriched for objects such aslocations, hour of day, and day of week. Normally, an embedding may betrained in a data-driven framework to preserve the semantic meaning ofobjects. For example, features of user set U, user location set L,destination category set CATE, hour of day set HOD, and day of week setDOW in the search history 107 may be embedded as follows:

E({U})=[[U _(1,1) ,U _(1,2) , . . . ,L _(1,S) ], . . . , [U _(Q,1) ,U_(Q,2) , . . . ,U _(Q,S)]]

E({L})=[[L _(1,1) ,L _(1,2) , . . . ,L _(1,S) ], . . . , [L _(Q,1) ,L_(Q,2) , . . ,L _(Q,S)]]

E({CATE})=[[CATE_(1,1),CATE_(1,2), . . . ,CATE_(1,S)], . . .,[CATE_(K,1),CATE_(K,2), . . . ,CATE_(K,S)]]

E({HOD})=[[HOD_(1,1),HOD_(1,2), . . . ,H_(1,S)], . . .,[HOD_(24,1),HOD_(24,2), . . . ,HOD_(24,S)]]

E({DOW})=[[DOW_(1,1),DOW_(1,2), . . . ,DOW_(1,S)], . . . ,[DOW_(7,1),DOW_(7,2), . . . ,DOW_(7,S)]]

where S denotes a pre-defined feature size of the embedding vectors, Qdenotes the size of potential locations (destinations) in the searchhistory 107, and K denotes the size of location categories in the searchhistory 107, respectively. Therefore, any search event (u, l, k, h, d)that user u searched l-th location that is under k-th category at h-thhour of day and d-th day of week can be encoded as

E(u, l, k, h, d)=(lookup_(u) E({U}), loookup_(l) E([L]), lookup_(k)E({CATE}), lookup_(h) E({HOD}), lookup_(d) E([DOW]))

where lookup_(i)(E) is an operation that extract i-th row (embeddingvector) from the embedding matrix E. The raw input 105 determines whichrow (embedding vector) is extracted. Thus, the user (u), the givensearch context (k, h, d), and the candidate destination (l) may be usedas pointers to entries of the embedding matrix. The extracted vector maybe of (S, 1)-size. In the illustrated example, E(u, l, k, h, d) resultsin a (S×5) matrix since we have 5 embedding vectors 202-1, . . . , 202-5of respective feature size S. Of course, different numbers of embeddingvectors and respective feature sizes are possible to encode searchevents.

Context Modeling Layer

An example of context modeling layer 300 of machine learning circuitry110 is illustrated in FIG. 3. In context modeling layer 300, a goal is,given feature-processed contextual information E(u, l, k, h, d) ofsearch event r, i.e., corresponding embedding vectors 202-1, . . . ,202-5, to construct its semantic embedding. Context modeling layer 300may be a supervised learning algorithm which computes semanticrepresentation for (historic) search events by respecting theirsimilarity to a given query/context.

A detailed example calculation of a semantic embedding 204 for searchevent r may be as follows:

emb=(concatenate_(axis=1)(E(U _(u)), E(L _(l)), E(CATE_(k)), E(DOW_(d)),E(HOD_(h)))×w+b)

where concatenate_(axis=1)( ) denoted an operation that concatenates thefive (S, 1)-size embedding vectors from the feature processing layeralong the second axis to generate a (S×5) matrix, and w and b are lineartransformation parameters that need to be trained. Although FIG. 3illustrates a single layer learning machine, search event inputs couldbe also mapped into a lowdimensional semantic space with a deep neuralnetwork (e.g. CNN) to achieve a highly nonlinear semantic embedding tomodel the human perception of location search semantics. The calculationof the semantic embedding 204 may be done for all candidatedestinations, given their respective feature-processed contextualinformation E(u, l, k, h, a) from the feature processing layer. Theoutput of context modeling layer 300 may this be one or more semanticembeddings 204 for one or more candidate destinations.

Ranking Layer

An embodiment of the one or more ranking layers 400 is illustrated inFIG. 4.

In the ranking layer(s) 400, learning to rank or machine-learned ranking(MLR) may be leveraged to rank the candidate destinations to predict themost possible search destination. MLR is the application of machinelearning, typically supervised, semi-supervised or reinforcementlearning, in the construction of ranking models for informationretrieval systems. Training data may comprise lists of items with somepartial order specified between items in each list. This order istypically induced by giving a numerical or ordinal score or a binaryjudgment (e.g. “relevant” or “not relevant”) for each item. The rankingmodel purposes to rank, i.e. producing a permutation of items in new,unseen lists in a similar way to rankings in the training data. Here,listwise MLR may be applied to rank the list of candidate destinations.

For the convenience of MLR algorithms, search event—candidatedestination pairs may be represented by numerical vectors, the semanticembedding vectors 204. Components of semantic embedding vectors 204 maybe referred to as features, factors, or ranking signals. They may bedivided into three groups:

-   -   Query-independent or static features those features, which        depend only on the candidate destination, but not on the        search/query.    -   Query-dependent or dynamic features those features, which depend        both on the candidate destination and the query.    -   Query level features or query features, which depend only on the        query. For example, the number of letters or words in a query.

As can be seen from FIG. 4, a plurality of semantic embedding vectors204-0, . . . , 204-n (corresponding to the candidate destinations) fromcontext modeling layer 300 act as input to the ranking layer(s) 400which may be implemented as CNN or fully connected neural networkcomprising an input layer 402, one or more hidden layers 404, and anoutput layer 406. The output layer 406 may provide aprobability/likelihood y₁, . . . , y_(n) for each candidate destination.Block 410 may then apply a softmax function to y₁, . . . , y_(n).Softmax is a function that takes as input a vector of n real numbers andnormalizes it into a probability distribution comprising n probabilitiesproportional to the exponentials of the input numbers. The predicteddestination (ranked first) may then correspond to the candidatedestination with the highest probability.

The detailed calculation of search location prediction may be as follows

X={x _(i)}={emb_(i) }; H={h _(i)}=relu(X×w _(H) +b _(H)); Y={y_(i)}=relu(H×w _(Y)+b_(Y))

R={y _(i)}=softmax(Y)

d=argmax(R)

where relu denotes an activation function defined as the positive partof its argument relu x=max(0, x+), softmax( ) is an activation functiondefined as the normalized distribution of input value, and w, b are theparameters that need to be trained based on historic search data asground truth. Therefore, the target location d may be predicted bychoosing the highest score among all candidates R. If the predictiondoes not correspond to the user's truly desired destination (groundtruth), model parameters of the feature processing layer, contextmodeling layer and/or the ranking layer may be updated so as to minimizethe difference between prediction and ground truth.

The proposed concept aims to integrate user profile and contextinformation to provide destination predictions in different levels ofgranularity (for example, low—Category; medium—POI; high—streetaddress). A joint search result from the proposed model, the searchhistory 107, and third-party crowdsourcing like map service may enhancethe user experience in all three initially mentioned search scenarios(recall/find/discover). An example of returned search locations isillustrated in FIG. 5, wherein the location list by contextual modelingresults may be returned by the proposed concept.

The deployment of the proposed model on location search task wasexplored under three different scenarios: no user input, user types 1character, and user types 2 characters.

-   -   Dataset: User search events.    -   Raw feature: (user ID, search location name, timestamp), 220        users, 8,995 locations including POI name and street address, 8        months period.

Task:

Assume we have user search events

I _(t) _(T) ={(search location i ₀ at time t ₀), . . . , (searchlocation i _(T) at time t _(T))}, t∈w,

it aims to predict I_(t) _(T) ₊₁ 90% of total events as were used astraining dataset, where data contains both location i and timestamp tinformation for the search, and used the remaining 10% as the testdataset. The top 1-best and 5-best matching accuracy that is widely usedin recommendation system to measure the performance were applied.

Modeling:

-   -   User (u);    -   Temporal: hour-of-day (hod: h), day-of-week (dow: d);    -   Spatial: destination (d_(u)∈D_(u), D_(u)∈D, D_(u)∩D_(u′)=Π)        where D_(u) indicates the location candidates are from user u        search history, D represent all locations collected from all        users U. POI category (cate: k)

Evaluation Metrics:

-   -   1-best: exact match (EM);    -   5-best;

Baseline: among personal POI

-   -   P(d|D_(u)): destination search based on popularity among        personal location candidate; (11.2%, 32.4%)    -   P(d|D_(u)): destination search based on search recency among        personal location candidate; (6.0%, 8.1%)

Proposed algorithm:

-   -   P(d|u, k, h, d, D_(u)): contextual and personal search, among        personal location candidates (45.8%, 74.7%)    -   P(d|u, k, h, d, D): contextual and personal search, among        locations from all the users; (9%, 21%)

Remark:

1. Smart search significantly outperforms popularity search: 1-best(45.8% vs 11.2%) and 5-best (74.7% vs 32.4%)

2. Smart search significantly outperforms recency search: 1-best (45.8%vs 6.0%) and 5-best (74.7% vs 8.1%)

Performance: User Types 1 or 2 Characters

Evaluation Metrics:

-   -   EM (1-best);    -   5-best;

Baseline P(d D): popular destination search among all locations from allthe users (1567 per user in average); (9%, 21%) User types 1 character,among all POI but filtered by query (78 per user in average)

-   -   P(d|u, k, h, d, q, D): contextual and personal search; (32%,        55%)

User types 2 character, among all POI but filtered by query (10 per userin average)

-   -   P(d|u, k, h, d, q, D): contextual and personal search; (61%,        87%)

The skilled person having benefit from the present disclosure willappreciate that the proposed search concept may be well suited forenhancing navigational systems in smartphones and/or cars. Therefore,according to a further aspect of the present disclosure, it is alsoprovided a vehicle.

The vehicle 600 shown in FIG. 6 comprises machine learning circuitry 110configured to determine, based on a user and a given search context, atleast one candidate destination from historic location data associatedwith the user. The machine learning circuitry 110 is further configuredto determine, from an embedding matrix, a plurality of embedding vectors202 associated with the at least one candidate destination (l), the user(u), and the given context (t, q). The embedding matrix comprisesembedding vectors 202 for different (categorial) components of the input(u, l, t, q). The learning circuitry 110 comprises one or more contextmodeling machine learning network layers 300 configured to generate asemantic embedding 204 for the candidate destination based on thedetermined plurality of embedding vectors 202. The learning circuitry110 further comprises one or more ranking machine learning layers 400configured to generate a probability score for the candidate destinationbased on the semantic embedding 204 for the candidate destination. Thecircuitry is configured to generate a respective probability score for aplurality of candidate destinations and to predict the user's targetdestination by choosing the candidate destination having the highestprobability score.

To summarize, FIG. 7 illustrates a flowchart of a method 700 forpredicting a destination for a user.

Method 700 includes determining 710, based on historic search data 107associated with the user (u), at least one candidate destination (l)based on the user and a given context (t, q). Method 700 furtherincludes determining 720 a plurality of embedding vectors 202 associatedwith the at least one candidate destination, the user, and the givencontext from an embedding matrix. The embedding matrix comprisesembedding vectors for different components of the historic search data.The skilled person having benefit form the present disclosure willappreciate that determining 720 may include training the embeddingvectors 202 and then selecting trained embedding vectors 202. Method 700further includes feeding 730 the plurality of embedding vectors into oneor more first neural network layers to generate a semantic embedding 204for the candidate destination. Method 700 further includes feeding 740the semantic embedding 204 into one or more second neural network layersto generate a probability score for the candidate destination.

As indicated at 750, the method 700 may be performed for a plurality ofcandidate destinations to generate a respective probability score foreach of the candidate destinations, resulting in a ranking of thecandidate destinations. The user's target destination may be predictedby choosing the candidate destination having the highest probabilityscore or the highest rank among the plurality of ranked candidatedestinations.

Embodiments of the present disclosure may provide contextual,personalized and collaborative learning framework to improve searchexperience: The proposed concept leverages context information to modelsearch event. Not only the final prediction retains a good performancebut also the intermediate output such as object embedding 202 and userembedding 204 can be critical features for other downstream tasks, e.g.,segmentation.

Collaborative filtering among objects: As embedding location to processthe discrete value of its name, locations that a user might search onthe basis of reactions by similar users may be filtered out to enhancethe personalization in the search scenario “discover” or recommendation.

Rich semantic modeling: By using embeddings, the capabilities ofprevious Natural Language Processing (NLP) methods may be expanded bycreating contextual representations based on the surrounding contextwhich may lead to richer semantic models.

Good performance: The proposed algorithm may significantly outperformprior-art models.

Smart search experience: Physical context plays more critical role forsearch location for navigation than online searches as the user has tophysically drive toward the destination.

The aspects and features described in relation to a particular one ofthe previous examples may also be combined with one or more of thefurther examples to replace an identical or similar feature of thatfurther example or to additionally introduce the features into thefurther example.

Examples may further be or relate to a (computer) program including aprogram code to execute one or more of the above methods when theprogram is executed on a computer, processor or other programmablehardware component. Thus, steps, operations or processes of differentones of the methods described above may also be executed by programmedcomputers, processors or other programmable hardware components.Examples may also cover program storage devices, such as digital datastorage media, which are machine-, processor- or computer-readable andencode and/or contain machine-executable, processor-executable orcomputer-executable programs and instructions. Program storage devicesmay include or be digital storage devices, magnetic storage media suchas magnetic disks and magnetic tapes, hard disk drives, or opticallyreadable digital data storage media, for example. Other examples mayalso include computers, processors, control units, (field) programmablelogic arrays ((F)PLAs), (field) programmable gate arrays ((F)PGAs),graphics processor units (GPU), application-specific integrated circuits(ASICs), integrated circuits (ICs) or system-on-a-chip (SoCs) systemsprogrammed to execute the steps of the methods described above.

Embodiments may be based on using a machine-learning model ormachine-learning algorithm. Machine learning may refer to algorithms andstatistical models that computer systems may use to perform a specifictask without using explicit instructions, instead relying on models andinference. For example, in machine-learning, instead of a rule-basedtransformation of data, a transformation of data may be used, that isinferred from an analysis of historical and/or training data. Forexample, the content of images may be analyzed using a machine-learningmodel or using a machine-learning algorithm. In order for themachine-learning model to analyze the content of an image, themachine-learning model may be trained using training images as input andtraining content information as output. By training the machine-learningmodel with a large number of training images and/or training sequences(e.g. words or sentences) and associated training content information(e.g. labels or annotations), the machine-learning model “learns” torecognize the content of the images, so the content of images that arenot included in the training data can be recognized using themachine-learning model. The same principle may be used for other kindsof sensor data as well: By training a machine-learning model usingtraining sensor data and a desired output, the machine-learning model“learns” a transformation between the sensor data and the output, whichcan be used to provide an output based on non-training sensor dataprovided to the machine-learning model. The provided data (e.g. sensordata, meta data and/or image data) may be preprocessed to obtain afeature vector, which is used as input to the machine-learning model.

Machine-learning models may be trained using training input data. Theexamples specified above use a training method called “supervisedlearning”. In supervised learning, the machine-learning model is trainedusing a plurality of training samples, wherein each sample may comprisea plurality of input data values, and a plurality of desired outputvalues, i.e. each training sample is associated with a desired outputvalue. By specifying both training samples and desired output values,the machine-learning model “learns” which output value to provide basedon an input sample that is similar to the samples provided during thetraining. Apart from supervised learning, semi-supervised learning maybe used. In semi-supervised learning, some of the training samples lacka corresponding desired output value. Supervised learning may be basedon a supervised learning algorithm (e.g. a classification algorithm, aregression algorithm or a similarity learning algorithm. Classificationalgorithms may be used when the outputs are restricted to a limited setof values (categorical variables), i.e. the input is classified to oneof the limited set of values. Regression algorithms may be used when theoutputs may have any numerical value (within a range). Similaritylearning algorithms may be similar to both classification and regressionalgorithms but are based on learning from examples using a similarityfunction that measures how similar or related two objects are. Apartfrom supervised or semi-supervised learning, unsupervised learning maybe used to train the machine-learning model. In unsupervised learning,(only) input data might be supplied and an unsupervised learningalgorithm may be used to find structure in the input data (e.g. bygrouping or clustering the input data, finding commonalities in thedata). Clustering is the assignment of input data comprising a pluralityof input values into subsets (clusters) so that input values within thesame cluster are similar according to one or more (pre-defined)similarity criteria, while being dissimilar to input values that areincluded in other clusters.

Reinforcement learning is a third group of machine-learning algorithms.In other words, reinforcement learning may be used to train themachine-learning model. In reinforcement learning, one or more softwareactors (called “software agents”) are trained to take actions in anenvironment. Based on the taken actions, a reward is calculated.Reinforcement learning is based on training the one or more softwareagents to choose the actions such, that the cumulative reward isincreased, leading to software agents that become better at the taskthey are given (as evidenced by increasing rewards).

Furthermore, some techniques may be applied to some of themachine-learning algorithms. For example, feature learning may be used.In other words, the machine-learning model may at least partially betrained using feature learning, and/or the machine-learning algorithmmay comprise a feature learning component. Feature learning algorithms,which may be called representation learning algorithms, may preserve theinformation in their input but also transform it in a way that makes ituseful, often as a pre-processing step before performing classificationor predictions. Feature learning may be based on principal componentsanalysis or cluster analysis, for example.

In some examples, anomaly detection (i.e. outlier detection) may beused, which is aimed at providing an identification of input values thatraise suspicions by differing significantly from the majority of inputor training data. In other words, the machine-learning model may atleast partially be trained using anomaly detection, and/or themachine-learning algorithm may comprise an anomaly detection component.

In some examples, the machine-learning algorithm may use a decision treeas a predictive model. In other words, the machine-learning model may bebased on a decision tree. In a decision tree, observations about an item(e.g. a set of input values) may be represented by the branches of thedecision tree, and an output value corresponding to the item may berepresented by the leaves of the decision tree. Decision trees maysupport both discrete values and continuous values as output values. Ifdiscrete values are used, the decision tree may be denoted aclassification tree, if continuous values are used, the decision treemay be denoted a regression tree.

Association rules are a further technique that may be used inmachine-learning algorithms. In other words, the machine-learning modelmay be based on one or more association rules. Association rules arecreated by identifying relationships between variables in large amountsof data. The machine-learning algorithm may identify and/or utilize oneor more relational rules that represent the knowledge that is derivedfrom the data. The rules may e.g. be used to store, manipulate or applythe knowledge.

Machine-learning algorithms are usually based on a machine-learningmodel. In other words, the term “machine-learning algorithm” may denotea set of instructions that may be used to create, train or use amachine-learning model. The term “machine-learning model” may denote adata structure and/or set of rules that represents the learned knowledge(e.g. based on the training performed by the machine-learningalgorithm). In embodiments, the usage of a machine-learning algorithmmay imply the usage of an underlying machine-learning model (or of aplurality of underlying machine-learning models). The usage of amachine-learning model may imply that the machine-learning model and/orthe data structure/set of rules that is the machine-learning model istrained by a machine-learning algorithm.

For example, the machine-learning model may be an artificial neuralnetwork (ANN). ANNs are systems that are inspired by biological neuralnetworks, such as can be found in a retina or a brain. ANNs comprise aplurality of interconnected nodes and a plurality of connections,so-called edges, between the nodes. There are usually three types ofnodes, input nodes that receiving input values, hidden nodes that are(only) connected to other nodes, and output nodes that provide outputvalues. Each node may represent an artificial neuron. Each edge maytransmit information, from one node to another. The output of a node maybe defined as a (non-linear) function of its inputs (e.g. of the sum ofits inputs). The inputs of a node may be used in the function based on a“weight” of the edge or of the node that provides the input. The weightof nodes and/or of edges may be adjusted in the learning process. Inother words, the training of an artificial neural network may compriseadjusting the weights of the nodes and/or edges of the artificial neuralnetwork, i.e. to achieve a desired output for a given input.

Alternatively, the machine-learning model may be a support vectormachine, a random forest model or a gradient boosting model. Supportvector machines (i.e. support vector networks) are supervised learningmodels with associated learning algorithms that may be used to analyzedata (e.g. in classification or regression analysis). Support vectormachines may be trained by providing an input with a plurality oftraining input values that belong to one of two categories. The supportvector machine may be trained to assign a new input value to one of thetwo categories. Alternatively, the machine-learning model may be aBayesian network, which is a probabilistic directed acyclic graphicalmodel. A Bayesian network may represent a set of random variables andtheir conditional dependencies using a directed acyclic graph.Alternatively, the machine-learning model may be based on a geneticalgorithm, which is a search algorithm and heuristic technique thatmimics the process of natural selection.

It is further understood that the disclosure of several steps,processes, operations or functions disclosed in the description orclaims shall not be construed to imply that these operations arenecessarily dependent on the order described, unless explicitly statedin the individual case or necessary for technical reasons. Therefore,the previous description does not limit the execution of several stepsor functions to a certain order. Furthermore, in further examples, asingle step, function, process or operation may include and/or be brokenup into several sub-steps, -functions, -processes or -operations.

If some aspects have been described in relation to a device or system,these aspects should also be understood as a description of thecorresponding method. For example, a block, device or functional aspectof the device or system may correspond to a feature, such as a methodstep, of the corresponding method. Accordingly, aspects described inrelation to a method shall also be understood as a description of acorresponding block, a corresponding element, a property or a functionalfeature of a corresponding device or a corresponding system.

The following claims are hereby incorporated in the detaileddescription, wherein each claim may stand on its own as a separateexample. It should also be noted that although in the claims a dependentclaim refers to a particular combination with one or more other claims,other examples may also include a combination of the dependent claimwith the subject matter of any other dependent or independent claim.Such combinations are hereby explicitly proposed, unless it is stated inthe individual case that a particular combination is not intended.Furthermore, features of a claim should also be included for any otherindependent claim, even if that claim is not directly defined asdependent on that other independent claim.

1. Method for predicting a destination for a user, the methodcomprising: determining, based on historic search data associated withthe user, at least one candidate destination based on the user and agiven context; determining a plurality of embedding vectors associatedwith the at least one candidate destination, the user, and the givencontext from an embedding matrix, the embedding matrix comprisingembedding vectors for different components of the historic search data;feeding the plurality of embedding vectors into one or more first neuralnetwork layers to generate a semantic embedding for the candidatedestination; and feeding the semantic embedding into one or more secondneural network layers to generate a probability score for the candidatedestination.
 2. The method of claim 1, wherein the context is indicativeof at least one of a current time, a current location of the user,current weather conditions, current traffic conditions.
 3. The method ofclaim 2, wherein the context “current time” is decomposed into twofeatures “hour of day” and “day of week”.
 4. The method of claim 1,wherein the at least one candidate destination is determined from atleast one of the user's personal search history, a query of the user, orthe given context.
 5. The method of claim 1, wherein the method isperformed for a plurality of candidate destinations to generate arespective probability score for each of the candidate destinations. 6.The method of claim 5, comprising predicting the user's targetdestination by choosing the candidate destination having the highestprobability score or the highest rank among the plurality of candidatedestinations.
 7. The method of claim 6, further comprising adjustingcomputational weights of the first and/or the second neural networklayers if the user selects a candidate destination as target destinationnot having the highest probability score or the highest rank to minimizea difference between the model prediction and ground truth correspondingto the user's selection.
 8. The method of claim 1, further comprisingtraining the embedding matrix based on the search history by adjustingcomputational weights of one or more embedding layers to mapsemantically similar components of the historic location data togeometrically close embedding vectors in a common semantic space.
 9. Themethod of claim 1, wherein the embedding matrix comprises U userembedding vectors corresponding to U users, Q location embedding vectorscorresponding to Q searched locations, K category embedding vectorscorresponding to K destination categories, 24 hour embedding vectorscorresponding to 24 hours per day, and seven day embedding vectorscorresponding to seven days per week.
 10. Apparatus for predicting adestination for a user, the apparatus comprising circuitry configured todetermine, based on historic search data associated with the user, atleast one candidate destination based on the user and a given context;determine a plurality of embedding vectors associated with the at leastone candidate destination, the user, and the given context from anembedding matrix, the embedding matrix comprising embedding vectors fordifferent components of the historic search data; feed the plurality ofembedding vectors into one or more first neural network layersconfigured to generate a semantic embedding for the candidatedestination; and feed the semantic embedding into one or more secondneural network layers configured to generate a probability score for thecandidate destination.
 11. The apparatus of claim 10, wherein thecircuitry is configured to generate a respective probability score for aplurality of candidate destinations and to predict the destination bychoosing the candidate destination having the highest probability score.12. The apparatus of claim 11, wherein the circuitry is configured toadjust computational weights of the first and/or the second neuralnetwork layers if the user selects a candidate destination as targetdestination not having the highest probability score.
 13. The apparatusof claim 10, wherein the circuitry is configured to train the embeddingmatrix based on the historic search data by adjusting computationalweights of one or more third neural network layers to map semanticallysimilar components of the historic search data to geometrically closeembedding vectors.
 14. A vehicle, comprising circuitry configured todetermine, based on historic search data associated with the user, atleast one candidate destination based on the user and a given context;determine a plurality of embedding vectors associated with the at leastone candidate destination, the user, and the given context from anembedding matrix, the embedding matrix comprising embedding vectors fordifferent components of the historic search data; feed the plurality ofembedding vectors into one or more first neural network layersconfigured to generate a semantic embedding for the candidatedestination; and feed the semantic embedding into one or more secondneural network layers configured to generate a probability score for thecandidate destination, wherein the circuitry is configured to generate arespective probability score for a plurality of candidate destinationsand to predict a user's target destination by choosing the candidatedestination having the highest probability score.